Btw, if you have relational data and a few good people with strong computer science backgrounds rather than statisticians or mathematicians, have a look at Inductive Logic Programming. ILP is a set of machine learning techniques that learn logic programs from logic programs. The sample efficiency is on a class of its own and it generalises robustly from very little data[1].
I study ILP algorithms for my PhD. My research group has recently developed a new technique, Meta Interpretive Learning. Its canonical implementation is Metagol:
Please feel free to email me if you need more details. My address is in my profile.
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[1] As a source of this claim I always quote this DeepMind paper where Metagol is compared to the authors' own system (which is itself an ILP system, but using a deep neural net):
ILP has a number of appealing features. First, the learned program is an
explicit symbolic structure that can be inspected, understood, and verified.
Second, ILP systems tend to be impressively data-efficient, able to generalise
well from a small handful of examples. The reason for this data-efficiency is
that ILP imposes a strong language bias on the sorts of programs that can be
learned: a short general program will be preferred to a program consisting of
a large number of special-case ad-hoc rules that happen to cover the
training data. Third, ILP systems support continual and transfer learning. The
program learned in one training session, being declarative and free of
side-effects, can be copied and pasted into the knowledge base before the next
training session, providing an economical way of storing learned knowledge.
I study ILP algorithms for my PhD. My research group has recently developed a new technique, Meta Interpretive Learning. Its canonical implementation is Metagol:
https://github.com/metagol/metagol
Please feel free to email me if you need more details. My address is in my profile.
___________________
[1] As a source of this claim I always quote this DeepMind paper where Metagol is compared to the authors' own system (which is itself an ILP system, but using a deep neural net):
https://arxiv.org/abs/1711.04574
ILP has a number of appealing features. First, the learned program is an explicit symbolic structure that can be inspected, understood, and verified. Second, ILP systems tend to be impressively data-efficient, able to generalise well from a small handful of examples. The reason for this data-efficiency is that ILP imposes a strong language bias on the sorts of programs that can be learned: a short general program will be preferred to a program consisting of a large number of special-case ad-hoc rules that happen to cover the training data. Third, ILP systems support continual and transfer learning. The program learned in one training session, being declarative and free of side-effects, can be copied and pasted into the knowledge base before the next training session, providing an economical way of storing learned knowledge.